A post-event stratified random sampling scheme for monitoring event-based water quality using an automatic sampler

摘要

Short rainfall events contribute to large portions of annual sediment and nutrient exports. Most water quality sampling schemes rely on regularly spaced temporal sampling and increasingly monitoring schemes are including a form of event-based sampling. A typical approach is to sample each event using equal intervals in time using an automatic sampler. The use of this form of sampling is systematic in nature and requires model-based statistics to be analysed correctly. Probabilistic based sampling methods allow for easier and more defendable statistical inference as the assumptions are not based on a model, rather they are based on the sample design. Several probabilistic methods have been developed, however these methods commonly require additional hardware to implement. In this paper we present a method using a stratified random sampling procedure for automatic samplers which does not require any additional hardware. Our approach is to divide the mean event hydrograph into strata based on key features such as the rising and falling limbs. Random sampling is applied within each strata. A problem of this approach is that the length of the event and strata must be defined before each event. We therefore outline how the samples can be post-stratified after each event based on the key hydrological components of each event. The sampling scheme is outlined using continuously sampled electrical conductivity and turbidity data of three events from a creek in south eastern Australia. Limited to 24 samples per event, estimated event mean CIs were within the observed event means for all three events. This method provides a flexible low-cost sampling scheme providing unbiased estimates of key event hydrological components which can be easily adapted by catchment management authorities.